Encryption: Whereas this approach doesn’t eliminate personal details from a set of data ? it renders the information unreadable without the decryption key. This way ? it cannot be abused even if it ends up in the wrong hands.
Generalization: An example of this technique is the use of age ranges This one rather than specific ages or narrowing down phone number details to the area codes.
Hashing: As is the case with encryption ? this one doesn’t involve the removal of This one fingerprintable fragments of data. Instead ? it substitutes them with random-looking hash strings.
The idea behind this method is
Pseudonymization: to replace personally identifiable telegram data elements of data entry with artificial records known as pseudonyms. Whereas anonymization is an irreversible process ? pseudonymization allows for re-identifying the cloaked data via specific clues at a later point.
Perturbation: altering PII values. It isn’t suitable for scenarios requiring accurate data ? though.
Randomization: Personal data fields are deliberately skewed or substituted with random attributes.
Suppression: This technique “strips” data by completely obliterating personally identifiable values from it.
The optimal choice depends on
There is no one-size-fits-all technique. a range of factors ? including appropriate questions to establish contact with the clientthe peculiarities of local legislation ? privacy guidelines at the industry level ? as well as the type and intended use of the data to be anonymized. Some organizations take a shortcut by outsourcing this task to specially crafted anonymization tools such as IBM Security Guardium or Oracle Advanced Security.
A fairly simple yet effective way to anonymize data is to leverage the common canada cell numbers practice IP anonymization module provided by Google Analytics. Website owners can opt for this instrument to align their projects with privacy laws such as GDPR.